Create app.py

app.py

@@ -0,0 +1,16 @@
+from transformers import AutoModelForMaskedLM
+from transformers import AutoTokenizer
+import spacy
+import pytextrank
+
+model_checkpoint = "vives/distilbert-base-uncased-finetuned-cvent-2019_2022"
+model = AutoModelForMaskedLM.from_pretrained(model_checkpoint, output_hidden_states=True)
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+
+FILT_GROUPS = ["CARDINAL", "TIME", "DATE", "PERCENT", "MONEY", "QUANTITY", "ORDINAL"]
+POS = ["NOUN", "PROPN", "VERB"]
+
+nlp = spacy.load("en_core_web_sm")
+nlp.add_pipe("textrank", last=True, config={"pos_kept": POS, "token_lookback": 3})
+all_stopwords = nlp.Defaults.stop_words